JOINT RESEARCH CENTER FOR PANEL STUDIESSELECTED DISCUSSION PAPER SERIESSDP2012-009
March, 2013Children’s Academic Ability and Family Background:
Findings from the Japan Child Panel Survey 2011
Hideo Akabayashi*
Ryosuke Nakamura**
Michio Naoi***
Jun Yamashita****Chizuru Shikishima*****
Keita Shinogaya*****【Abstract】

This paper reports findings about the relationship between children’s academic ability andfamily background using the “Japan Child Panel Survey 2011 (JCPS2011)” to serve as afollow-up of Akabayashi et al. (2011) that described the results of the JCPS2010. For thisanalysis, we pay particular attention to how the child’s gender, birth month, presence of siblings,
parents’ educational attainment, and level of household income impact scores in mathematics,
Japanese, and reasoning tests. The results of this study are the followings: (1) Father’s
educational attainment of a college degree or higher positively influences the scores inmathematics and Japanese; (2) Mother’s educational attainment of a college degree or higher
positively influences the scores in mathematics, Japanese, and reasoning; (3) Child’s birth
month between January 1 and April 1 negatively influences the scores in mathematics; (4)Household annual income positively influences the scores in mathematics, Japanese, andreasoning* Faculty of Economics, Keio University**

Graduate School of Economics, Keio University***

Tokyo University of Marine Science and Technology, Faculty of Marine Technology****

Faculty of Commerce, Chuogakuin University*****

Keio Advanced Research Centers

Joint Research Center for Panel Studies
Keio University

Children’s Academic Ability and Family Background:
Findings from the Japan Child Panel Survey 20111
Hideo Akabayashi (Faculty of Economics, Keio University)
Ryosuke Nakamura (Graduate School of Economics, Keio University)
Michio Naoi (Tokyo University of Marine Science and Technology, Faculty of Marine Technology)
Jun Yamashita (Faculty of Commerce, Chuogakuin University)
Chizuru Shikishima (Keio Advanced Research Centers)
Keita Shinogaya (Keio Advanced Research Centers)

SummaryThis paper reports findings about the relationship between children’s academic ability and family
background using the “Japan Child Panel Survey 2011 (JCPS2011)” to serve as a follow-up of
Akabayashi et al. (2011) that described the results of the JCPS2010. For this analysis, we pay
particular attention to how the child’s gender, birth month, presence of siblings, parents’
educational attainment, and level of household income impact scores in mathematics, Japanese, andreasoning tests. The results of this study are the followings: (1) Father’s educational attainment of acollege degree or higher positively influences the scores in mathematics and Japanese; (2) Mother’s
educational attainment of a college degree or higher positively influences the scores in mathematics,
Japanese, and reasoning; (3) Child’s birth month between January 1 and April 1 negatively
influences the scores in mathematics; (4) Household annual income positively influences the scoresin mathematics, Japanese, and reasoning.

Corresponding author: H. Akabayashi (email: hakab@econ.keio.ac.jp). Originally
Published as Chapter 2 in Y. Higuchi, T. Miyauchi, C. R. McKenzie and Keio University
Joint Research Center for Panel Studies (eds.), Oyako Kankei to Kakei Koudou no
Dainamizumu (Dynamism of Parent-Child Relationship and Household Behavior). Keio
University Press, pp. 25–48, in June 2012 in Japanese. This English version was translated
into by an external translation service supplier in March 2013. Note that information
included in this paper is as of March 2012.
1

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Section 1

Introduction2

Triggered by a national discussion over a decline in academic ability and the resulting social problems,
Japanese society has entered “the period of the academic ability survey” in order to collect informationon the situation (Kariya, 2009). As a result, based on the historical comparison of international academic
ability surveys such as the Program for International Student Assessment (PISA) and the Trends inInternational Mathematics and Science Study (TIMSS), researchers discovered that the academic abilityof children and students has declined rapidly (Sato, 2009). In addition, a comparison of the basic
academic abilities of mathematics and Japanese, using the same questions and the same school groups as
those used 20 years ago (Kariya and Shimizu, 2004), showed a decline in the correct response rate
regarding content that was not mentioned or explanations that were omitted from the “Yutori Education(pressure-free education)” of the 1990s. These results revealed that children’s basic academic abilities
have actually decreased.

In 2002, the Ministry of Education, Culture, Sports, Science and Technology (hereafter MEXT)
promoted the “Encouragement for Learning” program to address the decline in academic ability bychanging the previous policy of Yutori Eduation. One of the program’s new slogans was “Solid
Scholastic Ability.” In this policy, it is worth noting that the acquisition of basic knowledge and theestablishment of study habits in the home were included as education goals. If only taking note of thefact that the acquisition of basic knowledge is specified in this policy, it would seem that the “pendulumof education” (Shimizu, 2005) has swung back from pressure-free education to the postwar education
model that strongly emphasized knowledge. However, considering that students’ daily study habits andacquisition of learning skill for lifelong learning are also emphasized, the nature of academic ability
targeted by the governmental guidelines for teaching seems to be greatly different from that of the 1960s

2

This paper was written in response to the report by Akabayashi et al. (2011) describing the results of thechildren’s survey that was performed together with the Japan Household Panel Survey (JHPS) in 2010, whichmeans that we conducted a similar analysis and similar study as that of Akabayashi et al. In this analysis, weused the individual data obtained from the Keio Household Panel Survey (KHPS) in the Keio/Kyoto Joint
Global Center of Excellence Program and the individual data obtained from the Japan Child Panel Survey(JCPS) in the Joint Research Center for Panel Studies at Keio University. The analysis of this paper was
supported by the Grant-in-Aid for Scientific Research (A) 20243020. This paper was based on the report in
the Keio University Joint Research Center for Pane Studies workshop held at Keio University in November
2011. We acknowledge with gratitude the valuable and constructive comments for the report in the workshop
from Mr. Yoshio Higuchi, Mr. C.R. McKenzie, and all the participants and the analysis support of Ms. Hiroko
Araki, Mr. Takashi Hansaki, and Ms. Sachi Aizawa (all from the Graduate School of Economics, Keio
University).
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and 1970s. In other words, while acquisition of basic knowledge is targeted, the new program’s basic
concept focuses on acquiring knowledge supported by daily study habits and learning skills designed topromote lifelong learning, not a system that focuses on cramming education such that teachers force
children to acquire knowledge.

Based on these new education policies, various actions (e.g., actively imposing homework andestablishing a time for reading in the morning) have been developed on the educational front in order toimprove basic academic ability (Kageyama, 2010). The association between teachers offering theselearning tools and children’s academic ability has also been demonstrated (Tanaka, Kihara, and Ono,2009). In the field of educational psychology, not only methods of effective teaching and evaluation(Takagaki, Tazume, Nakatani, Ito, Kobayashi, and Mishima, 2011; Murayama, 2006; Suzuki, 2011) but
also the effect of instruction in specific learning skills (Shinogaya, 2011; Uesaka, 2010) have been
discovered to be important for the acquisition of knowledge and improved motivation.

However, in addition to the decline in academic ability itself, there is another problem related tochildren's and students’ academic ability in Japan, which is the widening “academic ability gap” (Sato,2009). Household sociocultural factors have been pointed to as a possible explanation of this widening
gap in academic ability. In previous academic ability surveys, it was shown that a child’s academic
ability was affected by his or her parents’ educational attainment and income (Kariya and Shimizu,
2004). It was also revealed that the correct response rate decreased remarkably for the “No study kids”
who did not study in any place other than a school, which indicated that the impact of school teaching ona child’s academic ability has decreased, whereas the impact of home environment has increased (Kariya,2008). Thus, considering that the home environment has an influence on children’s academic ability,
recent social changes such as growing socio-economic inequality and increasing poverty may haveresulted in the accelerated decline in academic ability and the widening academic ability gap acrosshousehold sociocultural factors (Uchida, 2007). Therefore, in order to address the two main issues ofacademic ability—decline in academic ability and academic ability gap—it may be important to clarify
the impact of household sociocultural factors such as parents’ educational attainment and income onchildren’s academic abilities as well as the process of this impact.

In this context, this paper examines the correlation between a child’s attributes and home environment
and the child’s academic abilities; the paper examines this correlation in light of the report by
Akabayashi et al. (2011) that discussed the association between academic ability and household budget,
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using JCPS2010 and JHPS2010. Specifically, by taking up household sociocultural factors such as theparents’ educational attainment and income, a distribution regarding the association between the values
and the test scores is identified, and statistical tests are performed. Then, the academic ability score isdefined as a dependent variable. Estimations are made using the ordinary least squares regression (OLS)
with children’s attributes and home environment as explanatory variables.

The next sections are organized in the following way: Section 2 describes the method for construction of
the academic ability scores in the subjects of the Japan Child Panel Survey 2011 that we used in thisstudy, and also outlines the distribution of academic ability and family background variables. Section 3
shows the association between a child’s attributes and family background variables and the child’s
academic abilities using charts. Section 4 illustrates the association between the household culturalenvironment variables and academic ability by using charts and summarizes the results of the statisticaltest. For the test, nonparametric analysis (Kruskal Wallis test and Wilcoxon-Mann-Whitney test) is
conducted. Section 5 shows the results estimated by the OLS of the impact of family background on
academic abilities. Section 6 provides future prospects.

Section 2

Data and indicators of academic ability

1. Data
In 2008, Keio University established a joint center, called the “Joint Research Center for Panel Studies at
Keio University,” in order to institute a panel study and accelerate research activities using the panel data.
In this study, we used the data from the “Japan Child Panel Survey” as a part of the activities of thiscenter.

In 2011, the Japan Child Panel Survey was conducted with children who belonged to the households thatwere participating in the Keio Household Panel Survey (KHPS).3 This survey, JCPS2011, included
1,126 subjects who were between the first grade in an elementary school and the third grade at a juniorhigh school as of March 2011. Before this survey, we requested additional cooperation for the Japan
Child Panel Survey from the subjects in the KHPS survey conducted in January 2011, and sent andobtained questionnaires by mail from the subjects who consented to participate in our study. As a gesture3

For more information of the Keio Household Panel Survey, refer to other studies (e.g., Seko et al. [2011]).
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of gratitude for cooperating in the survey, a book card equivalent to 500 yen and a plastic document
folder were offered to each child as a reward. For feedback purposes, the subjects were allowed to readthe summary of the individual survey results using their pre-assigned ID and password.

This survey consisted of the Children’s Questionnaire, designed to investigate a child’s basic academic
ability and study environment, and the Parent’s Questionnaire completed by the child’s parent as a
subject of KHPS. This Children’s Questionnaire included the achievement tests for mathematics,
Japanese, and reasoning, as well as the questionnaire survey on the child’s study situation and quality oflife. Different questions according to school year were prepared and the subjects were asked to answer
them by themselves within a certain time limit (20 minutes).4 The questionnaire survey also included
questions on such issues as afterschool activities, favorite and least favorite study subjects, school life,and homework situation. The Parent’s Questionnaire included survey items such as the type of schoolthat the child attended, the size of the class that the child belonged to, the experience of an entrance
examination for private or national school, the time the child spent studying, the actual household
expenditures on education, and the child’s sociality. Parents who had two or more children in the survey
were asked to respond to the questionnaire individually for each child. The above survey was designed
based on the Japan Child Panel Survey 2010 (JCPS2010) for the subjects who were in the households of
the JHPS subjects in 2010, and its comparability was achieved. However, it was modified appropriately
according to existing findings.5

A total of 659 subjects responded to the achievement test in the Children’s Questionnaire. This is thenumber of respondents who cooperated with the JCPS2011, excluding the subjects who cooperated onlywith the Parent’s Questionnaire. Among these 659 subjects, 220 children were in the lower grades of
elementary school, 232 children were in the upper grades of elementary school, and 207 respondents
were junior high school students. Of these, 333 and 326 respondents were male and female, respectively.
For birth month, 158 respondents were born between April and June, 171 were born between July and
September, 165 were born between October and December, and 165 were born between January andMarch. For the number of siblings, 60 respondents belonged to the group of one sibling (an only child),4

A survey by Drop-Off /Pick-Up method may have some issues; for example, the third person including theparent may revise mistakes in the test after the child responded. Although all problems have not been solved,
we took measures in our study; children were requested to put a sticker in the questionnaire by themselves
just after it was completed.
5
For the summary of the JCPS2010, refer to the study by Shikishima et al. (2011) and the study by
Yamashita et al. (2011).
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333 respondents belonged to the group of two siblings, 205 respondents belonged to the group of threesiblings, 44 respondents belonged to the group of four siblings, and 17 respondents belonged to thegroup of five or more siblings.

2. Measures of academic ability
The measures of academic ability used to explain the relationship between a child’s academic ability andfamily background were standardized by the following methods. The allotment of marks per question ineach subject (mathematics, Japanese, and reasoning) was scored as 1, to be summarized simply, and thenwas standardized to a mean of 50 and a standard deviation of 10 by each school year. In Japan, the Zscore computed in this manner (i.e. “hensachi”) is generally used as a standardized score in the school
setting. The distributions of the Z score are shown in Figure 1.

Next, the adjustment of the Z scores between the grades is discussed. In the many analyses in this paper,
all the surveyed school years are pooled (for six school years of elementary school and for three school
years of junior high school). The merits and demerits of such an analysis were examined by Akabayashi
et al. (2011). However, we summarized a part of the statement by Akabayashi et al. (2011) below
because it is important for this paper, which also used similar data to that used in the study conducted by
Akabayashi et al. First, it should be kept in mind that the scores based on different tests depending onschool year are pooled in order to be analyzed. In this case, the scores are required to be adjusted by
Item Response Theory (IRT), which was virtually impossible, considering the characteristics of the test,
the number of the questions, and the sample size. However, when systematic estimation results would beobtained even if the simplest standardization method is used, the estimate can be considered robust.
Consequently, in this paper, we conduct analyses based on these statements.

Section 3

Child’s attributes and academic ability

This section examines the association between gender, birth month, number of siblings, and children’s
academic ability. In reference to the study conducted by Akabayashi et al. (2011), we also compare ourresults with those of the JCPS2010, which was performed together with the JHPS using the sameachievement test.

1. Influence of gender
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Figure 2 shows a comparison of the average level of academic ability in mathematics, Japanese, andreasoning for male and female children. In Figure 2, mathematics and reasoning scores seem to behigher for males than females (a difference of 1.2 points in mathematics; 0.7 points in reasoning),
whereas there is no difference between males and females in Japanese. These results are nearly similar to
the analysis of the JCPS2010.

2. Influence of birth month
Figure 3 shows a comparison of academic ability according to the four birth month groups (April toJune; July to September; October to December; January to March). In Figure 3, there is no remarkabledifference in academic ability among the birth month groups. However, for mathematics and reasoning,the mean values seems to be slightly lower just in the group of subjects born between January 1 andApril 1. These results are nearly similar to the analysis of the JCPS2010.

3. Influence of the number of siblings
Figure 4 shows a comparison of academic ability judging by the mean score in each subject according to
the number of siblings. In this figure, academic ability in every subject seems to gradually decline as thenumber of siblings increased. This tendency is observed more clearly in comparison to the analysis of
the JCPS2010.

4. Results of statistical hypothesis test
The association between children’s attributes and academic ability was tested to determine whether the
association was actually statistically significant (Table 1). For more information on the testing method,refer to the note in Table 1. The test results show that the variables with significant difference at a 5%
level are the number of siblings and birth order. For the number of siblings, there is a significant
difference in all three subjects. For birth order, there is a significant difference in mathematics andJapanese. These results are slightly different from the analysis of the JCPS2010, which shows asignificant association with birth month.

Section 4

Family background and academic ability

This section, by focusing on parents’ educational attainments and income level, examines the association
between academic ability and children’s socio-economic home environment. As in Section 3, referring toPage 7 / 38

the study conducted by Akabayashi et al. (2011), we performed a comparison with the results of theJCPS2010, which was performed together with the JHPS using the same achievement test.

1. Influence of parent’s educational attainment
Figure 5 shows the average child’s academic ability in relation to their mother’s highest educational
attainment (junior high school, high school, junior college/technical college, or 4-year college).6 First,
for the levels of academic ability in mathematics and Japanese, there is a remarkable association with themother’s highest educational level. Generally, a child’s academic ability scores tend to increase as themother’s educational attainment is higher. On the other hand, for reasoning, there is no definite
association, and the average academic ability scores tend to be higher in the groups of mothers whose
highest educational attainment is junior high school or college. However, it should be noted that the
sample size is relatively small for the group of mothers whose highest educational attainment is juniorhigh school, which results in a large standard error.

Figure 6 shows the association between father’s educational attainment and academic ability scores. In
this figure, for mathematics and Japanese, there is a strong positive correlation between the father’s
educational attainment and the average academic ability scores. On the other hand, the scores forreasoning are slightly associated with the father’s educational attainment, in contrast with the tendency
shown in the figure relating to the mother’s educational attainment. However, this association forreasoning is weaker than that for mathematics and Japanese. Taking account of the standard error of theacademic ability scores, this result seems not to lead to any definite conclusions. The above-mentioned
tendencies are observed more clearly in this study than in the analysis of the JCPS2010.

2. Influence of household income
Figure 7-1 shows the average academic ability of children from the quartile groups based on household
annual income in 2010, based on the survey items of the KHPS.

In this figure, for mathematics and Japanese, there is a clear positive correlation between household
annual income and academic ability scores; above all, the difference between the mean scores of thegroup with the lowest household annual income (first quartile) and the three other groups is remarkably
6

In this paper, we call 4-year college graduate simply as “college graduate.” In Japanese government
statistics, 4-year college is called “university” which is used in the JCPS questionares. Under our educationalclassification, the group belonging to “others” has not been shown on the graph. Further, there was no motherin the sample who indicated that they had completed graduate school.
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wide. For reasoning, although the mean score tends to decrease in the first quartile group, there is no
definite association between annual income and the mean score in the second or higher quartile group.
Figure 7-2 shows how academic ability gap by income quartile would change with the progress ofschool year (the nine grades in the sample are grouped into lower grades and upper grades at anelementary and junior high school). As a result, for mathematics, Japanese, and reasoning, there is atendency to neither expand nor reduce academic ability gap definitely by a shift in school year. However,
the mean score of the first quartile group is the lowest across all subjects and school years, which
supports the results obtained from Figure 7-1.

Figure 7-3 shows the association between parents’ employment status and children’s academic ability
according to regular and non-regular employment.7 This figure suggests that parents’ employment status
has two different impacts on children’s academic ability. In other words, the average child’s academic
ability is higher when the father has regular employment, whereas the average child’s academic ability islower when the mother has regular employment.

The analysis in this section shows a tendency similar to the analysis of the JCPS2010. However, it
should be noted that there were also similar issues that were pointed out in the JCPS2010. That is to say,
it is not easy to interpret this tendency as a causal relationship. There is a significant correlation between
the father’s educational attainment and the child’s academic ability; at the same time, it is expected thatthe educational attainment may be associated with employment status. In addition, in families with afull-time working mother, children might complete the achievement test in the parents’ absence, whichmight influence the results through differences in enforcement conditions of the test.

3. Results of statistical hypothesis test
We used a statistical test to assay the association between children’s academic ability and parents’
educational attainment and annual income, as mentioned in the previous paragraphs. The results of thetest are shown in Table 2. For parents’ educational attainment, the samples were classified according to
the educational attainment, which is the same as Figure 5 and 6, and then were tested to see whether
there was a difference in mean score between the groups. In addition, it was determined whether thechild’s academic ability was affected by each parent’s educational attainment (college graduate or

7Here, “regular” employment refers to the employment status of being hired and having working
arrangements corresponding to that of full-time employment. Further, in total, the unemployed sample has
been included in “Non-regular employment.”
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non-college graduate). For household annual income, the samples were classified according to quartileof annual income, and then were tested to see whether there was a difference in mean score between thegroups. For mathematics and Japanese, the tests showed there was a statistically significant difference inthe scores between the groups for all the comparisons. These results are consistent with the tendency
mentioned above. For the scores in reasoning, there is no difference associated with the mother’s
educational attainment, but for other variables showing other family backgrounds, significant association
was found at the 10% or higher level.

Section 5 Estimated results of OLS

This section describes the estimated results of the OLS. For mathematics and Japanese, the Z scores
standardized by each school year were used as a dependent variable. Simple tabulation was used in thenumber of correct responses for reasoning.8 The independent variables used in this estimation could be
mostly classified into two groups: a variable to show the child’s attribute and a variable to show thefamily background. For a variable to show the child’s attribute, gender (Female dummy), birth period(Being born between January 1 and April 1; dummy), and birth order (First-born child; dummy) wereused. For a variable to show family background, parent’s educational attainment (Father’s college
graduate or higher, dummy; Mother’s college graduate or higher, dummy), parent’s employment status(Father’s regular employment, dummy; Mother’s regular employment, dummy), and tax-included
household incomes were used. Table 3 shows descriptive statistics for these explanatory variables. In thefollowing estimation, two kinds of identification were used: household income controlled linearly andthe dummy variable based on quartiles. This is because of the possibility that the association between
household income and academic ability might not always be linear, according to the discussion about
Figure 7-1.

Table 4 shows estimated results of the OLS, and the numerical value in parentheses showsheteroskedasticity robust standard error. The following are the primary estimated results obtained
consistently without depending on identification of income:When a child has been born between January 1 and April 1, the mathematics score decreases byapproximately 2 points. On the other hand, when a child is a first-born child,the mathematics

8

Reasoning is the total score, between 0-4, of the raw scores. To ascertain the difference in the average scoreby each school year, the school year dummy was used when reasoning was a dependent variable. Of course it
is ideal to use the scores when the impact of school year has been removed; in this paper, simple tabulation
was used to compare scores with those of the study by Akabayashi et al. (2011).
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score rises by approximately 1.8 points.
When a father is a college graduate or higher, the mathematics score rises by approximately 2.5
points and the Japanese score also rises by approximately 2 points.

When a mother is a college graduate or higher, the mathematics score rises by approximately 2.5
points and the Japanese score rises by approximately 3.6–3.7 points. For the reasoning scores,
there is a rise in the raw score of approximately 0.3 points out of a total of 4 points (after thedifference of the scoring average between the school years was controlled).

When household income increases to 1 million yen, both mathematics and Japanese scores riseby approximately 0.04 points; for the scores of reasoning, the raw score rises approximately 0.04
points. In addition, for the effect of household income according to quartile group, thenonlinearity of the effect of income is suggested, as is originally expected; for mathematics andJapanese, the effect of the quartile is the largest.

Child’s gender and mother’s employment status showed no statistically-significant systematicdifference.

Finally, we summarize briefly the differences between the results of our analysis and the results of theprevious analysis using the JCPS2010 that was performed together with the JHPS (Akabayashi et al.,2011). For the children’s attributes, similarly to the previous analysis, there is a negative impact of beingborn between January 1 and April 1 on mathematics, but unlike the previous analysis, a positive impact
of being the first-born child on mathematics was also found. For parents’ attributes, just as in theprevious analysis, there is a positive impact of the father’s and mother’s educational attainment onmathematics and Japanese. The impact of the mother’s educational attainment on mathematics is stable,in particular, in our analysis. Positive impact of household income on the test scores tends to beremarkable in our analysis.

Section 6

Conclusion

In order to analyze our data in light of the report by Akabayashi et al. (2011), which described the resultsof the JCPS2010 and the JHPS2010, we focused on the association of variables indicating children’s
gender, birth month, the presence of siblings, parents’ educational attainment, and the level of householdincome with the scores of mathematics, Japanese, and reasoning. There were slight differences in somevariables, but basically results similar to those of the study conducted by Akabayashi et al. (2011) wereconfirmed.
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The main challenges for the future are similar to those found by Akabayashi et al. (2011). The firstchallenge is to analyze the mechanisms through which income difference impacts academic ability. It
can be thought that income differences may produce academic ability difference through an investmentin the education of a child (private school, cram school), and the parents’ education level and genetic
factors may additionally affect academic ability. Therefore, such a mechanism should be clarifiedcomprehensively. The second challenge is to determine the association between population
demographics (the group that a child belongs to) and academic ability; specifically, it is necessary to
clarify the relationship between region, school, and academic ability. The main purpose of the JCPS2011
is to clarify the impact of personal factors such as home environment on a child’s academic ability. By
linking the KHPS data to local information or school information, an analysis that considers population
factors may reveal how region and school impact a child’s academic ability. The third challenge is todetermine the association between academic ability and behaviors besides academic ability. By
simultaneously analyzing the determinants of both academic ability and behaviors besides academic
ability, we may discuss the multifaceted roles that home and school play in this process, which so far
have not been identified systematically.

Appendix: Characteristics of the sample

The subjects of the JCPS2011 used in the proof of this paper were respondents who participated in the
KHPS2011, conducted in January 2011, who had a child (or children) between first grade at anelementary school and third grade at a junior high school. In the beginning of the study, we confirm therepresentativeness of the JCPS2011 sample in reference to the study conducted by Yamashita et al.
(2011). Specifically, we examine the cooperation rate by each school year and the problem of whether
participation in this survey was prescribed by the respondent’s attributes.

The value of the cooperation rate for the KHPS2011 is defined as the number of cooperators (the numberof respondents in the JCPS) divided by the number of potential respondents (the number of subjects who
might cooperate with the JCPS), just as it was in the study conducted by Yamashita et al. (2011). As aresult, as shown in Table A-1, the cooperation rate of the lower grades at elementary school (first to third
graders at elementary school), the upper grades at elementary school (fourth to sixth graders atelementary school), and junior high school students were 68.5%, 59.3%, and 50.0%, respectively; thisindicates that the cooperation rate might tend to decrease as the level of education increases. As shown
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in Table A-2, which focuses on each school year, the cooperation rates for first graders, second graders,
third graders, fourth graders, fifth graders, and sixth graders at elementary school, and first graders,second graders, and third graders at junior high school were 63.0%, 72.7%, 69.2%, 61.0%, 56.9%,
60.1%, 52.8%, 56.4%, and 40.6%, respectively. The highest cooperation rate was observed in the second
graders at elementary school, and the lowest cooperation rate was observed in the third graders at juniorhigh school; the difference was 32.1%.

Then, using a binomial probit model, we examine whether the presence of responses might be prescribedby the attribute of the household budget. We looked at the following variables: (1) child’s school year;(2) child’s gender; (3) child’s birth period; (4) child’s birth order; (5) household annual income; (6)parents’ educational attainment (whether parents are college graduates or not); and (7) parents’
employment status (regular employment or not). Table A-3 shows the estimated results, and we
confirmed the following points. In the case where the sample included all school years, father’s andmother’s educational attainment are significantly positive. There was no significant variable in the firstto third graders at elementary school. In the fourth to sixth graders at elementary school, father’s
educational attainment was significantly positive. In first to third graders at a junior high school, father’sand mother’s educational attainment and mother’s employment status were significantly positive
individually. These results revealed the following points: (1) There is a tendency that the cooperation
rate might vary depending on children’s (student’s) school year and parents’ educational attainment; (2)A similar tendency was found in the JCPS2010, but the association was weaker than in this study(JCPS2011); (3) On the whole, except for the mother’s employment status, there is no association
between other individual and household attributes.

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Page 15 / 38

Figures and Tables

Table 1 Association between children’s attributes and academic ability: Results of tests of the statistical
hypothesis
Subject

Birth month(quarter)Birth period(April to June vs.
January to March)

GenderNumber of siblings

Birth order

Mathematics

―

＊

＊

＊＊＊

＊＊

Japanese

―

―

―

＊＊＊

＊＊

Reasoning

―

―

―

＊＊＊

―

Note: Null hypothesis indicates that “there is no difference in deviate between the groups.” For birth month (quarter), the differences
between four groups (April to June; July to September; October to December; January to March) are compared. For birth month (April toJune and January to March), the difference between only the two groups in parentheses are compared. The number of siblings isclassified into five groups (1-5 persons) and the birth order is classified into five groups (first to fifth). For two groups, theWilcoxon-Mann-Whitney test is used. For three or more groups, the Kruskal Wallis test is used. ***, **, and * indicate that estimatedcoefficient is statistically significant at 1%, 5%, and 10% levels, respectively.

Page 16 / 38

Table 2 Association between family background and children's academic ability: Results of tests of thestatistical hypothesis
Subject

Mother's
educational
attainment

College graduate,
Mother(Figure omitted)

Father's
educational
attainment

College graduate,
Father(Figure omitted)

Household annual
income(Quartile)

Mathematics

＊＊＊

＊＊＊

＊＊＊

＊＊＊

＊＊＊

Japanese

＊＊＊

＊＊＊

＊＊＊

＊＊＊

＊＊＊

Reasoning

―

＊

＊

＊＊＊

＊＊＊

Note: Null hypothesis indicates that “there is no difference in deviate between the groups.” Mother’s educational attainment is classified
into five groups (junior high school graduate; high school graduate; junior college/technical college; 4-year college graduate or higher).
Father’s educational attainment is classified into six groups (junior high school graduate; high school graduate; junior college/technicalcollege; 4-year college graduate; graduate school). Household income is classified into four groups (quartile). For two groups, theWilcoxon-Mann-Whitney test is used. For three or more groups, the Kruskal Wallis test is used. ***, **, and * indicate that estimatedcoefficient is statistically significant at 1%, 5%, and 10% levels, respectively.

Page 17 / 38

Table 3 Descriptive statistics of the variables related to family background used in OLS
Variable

MeanStandard
deviation

Minimum

MaximumChild's gender (Female = 1)

0.49

0.50

0

1

Child's birth period (Birthday between January 1 and April 1 = 1)

0.25

0.43

0

1

Child's birth order (First-born child = 1)

0.45

0.50

0

1

Father's educational attainment (College graduate or higher = 1)

0.41

0.49

0

1

Mother's educational attainment (College graduate or higher = 1)

0.17

0.37

0

1
1

Father's employment status (Regular employment = 1)

0.74

0.44

0

Mother's employment status (Regular employment = 1)

0.12

0.32

0

1

Household annual income (in millions of yen)

7.18

3.46

1.0

25.0

Page 18 / 38

Table 4 Relationship between children’s attributes and family background and academic ability:
Estimated results of OLS
Mathematics

Japanese

Reasoning

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient（S.E.）（S.E.）（S.E.）（S.E.）（S.E.）（S.E.）-1.0630-1.06630.24780.2467-0.0636-0.0661(0.7564)(0.7592)(0.7663)(0.7649)(0.1032)(0.1026)

Child's birth period (Birthday
between January 1 and April-2.0022**-1.9999**-0.9182-0.9603-0.1613-0.1553

1 = 1)(0.8725)(0.8780)(0.9146)(0.9054)(0.1216)(0.1222)

Child's birth order(First-born child = 1)1.8709**1.8295**1.3607*1.27120.1733*0.1667(0.7809)(0.7707)(0.7812)(0.7788)(0.1021)(0.1023)

Father's employment status

2.4802***2.6175***1.8065**2.0215**0.10000.1366(Regular employment = 1)

(0.8680)(0.8596)(0.8773)(0.9090)(0.1166)(0.1194)

Mother's employment status

2.5176***2.6144***3.6605***3.7427***0.2818**0.3013**(Regular employment = 1)

(0.9122)(0.9426)(0.8441)(0.8470)(0.1321)(0.1339)

Father's employment status-0.9809-1.4577-1.3671-2.0930**-0.1291-0.2181*(Regular employment = 1)

(0.8994)(0.9657)(0.8988)(0.9669)(0.1228)(0.1298)

Mother's employment status(Regular employment = 1)

-1.5262-1.1051-1.6840-1.1782-0.2085-0.1248(1.1736)(1.2079)(1.2262)(1.2259)(0.1595)(0.1599)0.5129***―――0.4885***―――0.0412**―――Child's gender (Female = 1)

Household annual income(0.1385)
Income category: Second
quartile―――

Income category: Third

―――

―――

―――

3.6413***―――3.9040***―――4.3775***4.6771***―――

4.9892***

―――

No

No

0.5280***(0.1548)―――(1.3579)

No

0.2623*(0.1487)(1.2284)(1.2231)No(0.0163)(1.1575)(1.1942)quartile
Grade; dummy

2.4059**(1.1611)quartile
Income category: Fourth(0.1167)

0.3513**(0.1718)

Yes

Yes

N

646

646

646

646

646

646

R2

0.08240.0777

0.06640.07160.04060.0459Note: ***, **, and * indicate that the estimated coefficient is statistically significant at 1%, 5%, and 10% levels, respectively. Simple
tabulation is used for the reasoning scores. Robust standard error is in parentheses.

Page 19 / 38

Figure 1 Distribution of standard score (histogram)

050Frequency

100

150

(1) Mathematics

0

10

20

30

40

20

30

40

50

60

70

80

90

100

50

60

70

80

90

100

Z scoreAll grade, width=3050Frequency

100

150

(2) Japanese010

Z scoreAll grade, width=3(Continued)

Page 20 / 38050

Frequency

100

150

(3) Reasoning

0

10

20

30

40

50

Z score60

70

All grade, width=3

Note: Vertical axis, frequency; Horizontal axis, Z score; the sample of all school years are pooled.

Page 21 / 38

80

90

100

Figure 2 Children’s gender and level of academic ability
60
58
56

Z score

54
52

50.60

50

49.38

50.01

49.99

50.36

Male

Female

Male

49.63

48
46
44
42
40
Male

FemaleMathematics

Japanese
Children's gender

FemaleReasoning

Note: The count of the average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 22 / 38

Figure 3 Birth month and level of academic ability

Z score(1) Mathematics
60
58
56
54
52
50
48
46
44
42
40

50.69

50.80

49.90

48.61

Born in between Born in between Born in between Born in between
April and June
July and
October andJanuary and
September
DecemberMarch

Z score(2) Japanese
60
58
56
54
52
50
48
46
44
42
40

50.23

49.92

50.44

49.42

Born in between Born in between Born in between Born in between
April and June
July and
October andJanuary and
September
December
March(Continued)

Page 23 / 38

Z score(3) Reasoning
60
58
56
54
52
50
48
46
44
42
40

50.93

50.24

49.67

49.20

Born in between Born in between Born in between Born in between
April and June
July and
October andJanuary and
September
December
March
Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 24 / 38

Figure 4 Number of siblings and level of academic ability

Z score(1) Mathematics
60
58
56
54
52
50
48
46
44
42
40
38
36
34

52.98
50.85

49.34

46.55
38.67

1

2

3
4
Number of siblings (inc. the subject)

5

41.46

6

(2) Japanese
60
58
56

Z score

54

52.30
51.47

52

48.34

50
48

46.30

42.97
42.52

46
44
42
40
1

2

3
4
5
Number of siblings (inc. the subject)(Continued)

Page 25 / 386

(3) Reasoning
60
58
56

Z score

54

51.63
51.05

52

48.99

50

45.26
47.03

48

42.34

46
44
42
40
1

2

3

4

5

6

Number of siblings (inc. the subject)
Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 26 / 38

Figure 5 Mother’s educational attainment and level of academic ability(1) Mathematics
60
58
56
53.07

Z score

54
52
50

50.54
46.98

48.23

Junior high school

High school

48
46
44
42
40

Juniorcollege/Technicalcollege

University(2) Japanese
60
58
56

53.81

Z score

54
52

49.19

50

49.78
48.45

48
46
44
42
40

Junior high school

High school(Continued)

Page 27 / 38

Juniorcollege/Technicalcollege

University

(3) Reasoning
60
58

53.35

56
52.27

Z score

54
52
50

49.79

49.13

High school

Juniorcollege/Technicalcollege

48
46
44
42
40

Junior high school

University

Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group. Unlike the data for fathers (Figure 6), there is no mother in the sample who selected graduate school.

Page 28 / 38

Figure 6 Father’s educational attainment and level of academic ability(1) Mathematics
60
58

55.50

56

Z score

54

51.93

52
50
48

48.72

48.72

High school

Juniorcollege/Technicalcollege

45.62

46
44
42
40

Junior high school

University

Graduate school(2) Japanese
60
58

54.85

56

Z score

54
52
50

51.52
47.35

49.06

48.16

48
46
44
42
40

Junior high school

High school

Juniorcollege/Technicalcollege(Continued)

Page 29 / 38

University

Graduate school(3) Reasoning
60
58
56

Z score

54

51.25
51.19

52
50

50.16

48.80

High school

Juniorcollege/Technicalcollege

46.31

48
46
44
42
40

Junior high school

University

Graduate school

Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 30 / 38

Figure 7-1 Family income and academic ability(1) Mathematics
60
58
56

Z score

54

52.49

52
50
48

49.41

50.75

47.08

46
44
42
40
First quartile

Second quartile

Third quartile Fourth quartile(2) Japanese
60
58
56

Z score

54
50.43

52
50
48

50.81

52.12

46.64

46
44
42
40
First quartile

Second quartile(Continued)

Page 31 / 38

Third quartile Fourth quartile(3) Reasoning
60
58
56

Z score

54

51.46

52
50

49.78

50.47

47.81

48
46
44
42
40
First quartile

Second quartile

Third quartile Fourth quartile

Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 32 / 38

Figure 7-2 Family income and academic ability (Divided based on school year)(1) Mathematics
56
54
Z score

52
50
48
46
44
The lower grades of
elementary school

The upper grades of
elementary school

Junior high schoolFirst quartile
Second quartile
Third quartile
Fourth quartile (Family income hierarchy)(2) Japanese56
54
Z score

52
50
48
46
44
The lower grades of
elementary school

The upper grades of
elementary schoolFirst quartile
Second quartile
Third quartile
Fourth quartile (Family income hierarchy)(Continued)

Page 33 / 38

Junior high school(3) Reasoning
56
54
Z score

52
50
48
46
44
The lower grades of
elementary school

The upper grades of
elementary schoolFirst quartile
Second quartile
Third quartile
Fourth quartile (Family income hierarchy)
Note: The count of average score is based on all grade-pooled samples of the deviation.

Page 34 / 38

Junior high school

Figure 7-3 Parent’s employment status and academic ability(1) Father’s employment status
60
58
56

Z score

54
52
50

50.31

49.14

49.35

50.24

49.80

50.07

Regularemployment

Non-regular
employment

Regularemployment

48
46
44
42
40
Non-regular
employment

Regularemployment

Mathematics

Non-regular
employment

Japanese
Father's employment status

Reasoning(2) Mother’s employment status
60
58
56

Z score

54
52
50

50.14

48.93

50.13

49.04

50.15

48.87

Non-regular
employment

Regularemployment

Non-regular
employment

Regularemployment

Non-regular
employment

Regularemployment

48
46
44
42
40

Mathematics

Japanese
Mother's employment status

Reasoning

Note: The count of average score is based on all grade-pooled samples of the deviation. The error bars show standard error for every
group.

Page 35 / 38

Table A-1 Cooperation rate of the Japan Child Panel Survey 2011 (Divided into 3 school groups)

First to third grader at elementary school

Fourth to sixth grader at elementary schoolFirst to third grader at junior high school

Total

Cooperative

Uncooperative

68.5%

31.5%

(220)

(101)

59.3%

40.7%

(232)

(159)

50.0%

50.0%

(207)

(207)

58.5%

41.5%

(659)

(467)

Note: The case of no response in the Children's Questionnaire is excluded. The number of respondents is in parentheses.

Page 36 / 38

Table A-2 Cooperation of the Japan Child Panel Survey 2011 (Divided into each school year)
Grade

Cooperation rate

First grader at elementary school

63.0%

Second grader at elementary school

72.7%

Third grader at elementary school

69.2%

Fourth grader at elementary school

61.0%

Fifth grader at elementary school

56.9%

Sixth grader at elementary school

60.1%

First grader at junior high school

52.8%

Second grader at junior high school

56.4%

Third grader at junior high school

40.6%

Page 37 / 38

Table A-3 Determinant of cooperative probability of the Japan Child Panel Survey 2011 to the childsurvey (Probit model)

Response cooperation; dummy (1 = cooperation,
0 = noncooperation)

Child's gender (Female= 1)

All samples

First to third
grader atelementary
schoolFourth to sixth
grader atelementary
schoolFirst to third
grader at juniorhigh school

Coefficient

Coefficient

Coefficient

Coefficient（S. E.)（S. E.)（S. E.)（S. E.)

0.00780.1932-0.22290.0259(0.0795)(0.1565)(0.1374)(0.1306)

Child's birth period (Birthday between January 1

0.1096-0.00100.15590.1110and April 1 = 1)

(0.0925)(0.1875)(0.1562)(0.1504)

0.0397

0.0976-0.0765

0.1010Child's birth order (First-born child = 1)

(0.0805)(0.1562)(0.1396)(0.1303)

Father's educational attainment (College graduate

0.3903***0.29080.5538***0.3436**or higher = 1)

(0.0943)(0.1921)(0.1618)(0.1505)

Mother's educational attainment (College

0.3011**0.12830.28570.5593**

graduate or higher = 1)

(0.1301)(0.2344)(0.2108)(0.2450)

Father's employment status (Regular employment-0.1148-0.0557-0.0914-0.1254= 1)(0.0943)(0.1812)(0.1693)(0.1499)

Mother's employment status (Regular

0.0686-0.3386-0.00110.3611*

employment = 1)

(0.1240)(0.2627)(0.2107)(0.1951)-0.00390.0189-0.0249-0.0046(0.0130)(0.0276)(0.0235)(0.0199)

Grade; dummy

Yes

Yes

Yes

Yes

N

1,078

305

374

399

R2

0.04740.02750.04260.0474

Household annual income

Note: ***, **, and * indicate that estimated coefficient is statistically significant at 1%, 5%, and 10% levels, respectively.

Page 38 / 38